The missions of the Computational Clinical Science Laboratory (CCS Lab) directed by Dr. Woo-Young Ahn are to (1) elucidate the neurocognitive mechanisms of decision-making using computational approaches and (2) develop transdiagnostic phenotypes and cost-effective markers of psychiatric disorders, especially for addictive/anxiety disorders, which can be readily translated into clinical practice. We believe developing such objective and affordable markers will innovate prevention and treatment programs and will have enormous implications for mental health and clinical practice. To accomplish the missions, we seek to identify the risk/protective factors of the psychiatric conditions, understand their underlying brain mechanisms, and use cutting-edge statistical algorithms to make accurate predictions based on these findings.

Specifically, our lab uses decision neuroscience as a framework to understand both normative and abnormal behavior, computational modeling to delineate the cognitive processes responsible for decision-making deficits, and neuroimaging (e.g., fMRI) methods to probe their neural substrates. In collaboration with our colleagues, the CCS Lab aims to develop a quick and standardized battery that can predict clinical outcomes using machine learning algorithms. The battery will combine behavioral, brain (functional and structural), clinical, and genetic measures.